CN111801043A

CN111801043A - Biological assay for non-invasive detection of drug use and physiological conditions

Info

Publication number: CN111801043A
Application number: CN201980013097.0A
Authority: CN
Inventors: J·芬凯尔
Original assignee: Childrens National Medical Center Inc
Current assignee: Childrens National Medical Center Inc
Priority date: 2018-01-19
Filing date: 2019-01-15
Publication date: 2020-10-20
Anticipated expiration: 2039-01-15
Also published as: KR20200112883A; CN111801043B; EP3740115A1; AU2019209323B2; WO2019143620A1; US20210045679A1; JP7279999B2; US11931171B2; IL275862A; AU2019209323A1; MX2020007661A; EP3740115A4; IL275862B1; JP2021511141A; CA3088518A1

Abstract

The present disclosure relates to a method and apparatus for determining a drug use or physiological characteristic of a patient. The present disclosure describes: acquiring a video sequence of an eye of a patient, the video sequence being a plurality of video frames; determining a frequency spectrum from pupil data of the video sequence; and determining a physiological characteristic or drug usage of the patient based on the spectrum. In an embodiment, at least one frequency can be detected based on the physiological property being explored.

Description

Biological assay for non-invasive detection of drug use and physiological conditions

Cross Reference to Related Applications

This application claims priority from U.S. provisional application No. 62/619,434 filed on 2018, 1, 19, the teachings of which are hereby incorporated by reference in their entirety for all purposes. The present application also relates to U.S. patent application No. 2015/0116665 filed on 9/19/2014, U.S. patent application No. 2017/0100061 filed on 10/11/2016, and U.S. patent No. 9,326,725 filed on 3/30/2011, the contents of which are incorporated herein by reference.

Background

Technical Field

The present disclosure relates to drug use and/or physiological damage and its effects on pupil iris tremor. In particular, the present disclosure describes utilizing pupillometry for detecting drug use and/or physiological damage.

Background

Pupillary control requires complex physiology involving many neuronal pathways. Thus, the pupillary behavior provides a window of access to the integrity and functionality of these neuronal pathways. In addition, pupillary behavior as indicated by contraction and expansion of the iris by the sphincters and the extensor muscles may reflect alterations or abnormalities in the metabolism or structure of the central nervous system. This link to the central nervous system makes the determination and identification of pathology critical in clinical and experimental settings, and suggests that assessment of pupil behavior may provide a mechanism for rapid detection and diagnosis of pathology.

However, pupil assessment is most commonly performed using a pen torch and visual subjective observation, although it is a routine practice in healthcare and is used in a variety of settings from first responders to intensive care units. This subjective method is hampered by inter-operator variability due to operator expertise, and thus, although it is a simple assessment method, it cannot provide fine data. For example, the information generated by the pencil torch method may be limited to the overall pupil characteristics, such as the presence of light reflections and a rough estimate of pupil size and symmetry. As will be expected, subtle changes that may be an important means of tracking clinical conditions such as brain trauma or viability following cardiopulmonary arrest cannot be assessed.

Even when more discriminating methods have been employed (such as pupillometers), widespread acceptance and deployment is slow. While these methods may be useful for assessing pupil size and responsiveness, they may be expensive and may require a separate device that provides raw data without interpretation, necessitating the introduction of trained professionals to evaluate the data, synthesize the information, and provide the consumer with appropriate guidance regarding appropriate intervention.

Thus, there is a need for effective and convenient assessment of pupil behavior that is expected to provide pupillary measurements that can be used to monitor, among other things, drug use, drug abuse, drug tolerance, and opioid hyperalgesia.

The foregoing "background" description is for the purpose of generally presenting the context of the disclosure. The inventors' work, to the extent it is described in this background section, as well as aspects of the description that may not otherwise qualify as prior art at the time of filing, are neither expressly nor impliedly admitted as prior art against the present disclosure.

Disclosure of Invention

According to an embodiment, the present disclosure relates to a device for assessing pupillary iris tremor in a patient.

In an embodiment, the present disclosure relates to an apparatus for assessing pupillary iris tremor in a patient, comprising: a display; and processing circuitry configured to transform experimental data and reference data of the pupil iris tremor of the patient by a frequency-based transformation; calculating a first parameter of the one or more selected parameters based on transformed experimental data of the pupil iris tremor of the patient; calculating a corresponding first parameter of the one or more selected parameters based on the transformed reference data; generating an indicator from the first parameter based on the experimental data and the corresponding first parameter based on the reference data, the generated indicator being a normalization of the first parameter and the corresponding first parameter; determining whether the generated indicator reaches a predetermined threshold, the predetermined threshold being related to a biological activity goal; and displaying, on the display and based on the determination, the assessment of the pupillary iris tremor of the patient, wherein the assessment of the pupillary iris tremor of the patient is an identification of an opioid as the bioactive target.

In embodiments, the present disclosure also relates to an apparatus for assessing pupillary iris tremor in a patient, comprising a display; and processing circuitry configured to calculate a first parameter of the one or more selected parameters based on experimental data of the pupil iris tremor of the patient; calculating a corresponding first parameter of the one or more selected parameters based on reference data of pupillary iris tremor; generating an indicator from the first parameter based on the experimental data and the corresponding first parameter based on the reference data, the generated indicator being a normalization of the first parameter and the corresponding first parameter; determining whether the generated indicator reaches a predetermined threshold, the predetermined threshold being related to a biological activity goal; and displaying the assessment of the pupillary iris tremor of the patient on the display and based on the determination.

In an embodiment, the present disclosure is also directed to an apparatus for assessing pupillary iris tremor of a patient comprising processing circuitry configured to calculate a first parameter of one or more selected parameters based on experimental data of the pupillary iris tremor of the patient; calculating a corresponding first parameter of the one or more selected parameters based on reference data of pupillary iris tremor; generating an indicator from the first parameter based on the experimental data and the corresponding first parameter based on the reference data, the generated indicator being a normalization of the first parameter and the corresponding first parameter; determining whether the generated indicator reaches a predetermined threshold, the predetermined threshold being related to a biological activity goal; and displaying the assessment of the pupil irises of the patient on a display and based on the determination.

Drawings

The disclosure and many of the attendant advantages thereof will be more readily appreciated as the same becomes better understood by reference to the following detailed description when considered in connection with the accompanying drawings, wherein:

FIG. 1 is a flow chart depicting processing of acquired data according to an exemplary embodiment of the present disclosure;

figure 2 is a graphical representation of pupil oscillation as isolated prior to spectral analysis according to an exemplary embodiment of the present disclosure;

FIG. 3 is a flow chart depicting the evaluation of a spectral analysis in accordance with an exemplary embodiment of the present disclosure;

figure 4 is a graphical representation of an assessment of pupillary light reflex after exposure to an opioid according to an exemplary embodiment of the present disclosure;

fig. 5 is a graphical representation of transformed data normalized to the baseline maximum drug effect for multiple drugs according to an exemplary embodiment of the present disclosure; and is

Fig. 6 is a hardware description of an apparatus according to an example embodiment of the present disclosure.

Detailed Description

The terms "a" or "an," as used herein, are defined as one or more than one. The term "plurality", as used herein, is defined as two or more than two. The term another, as used herein, is defined as at least a second or more. The terms including and/or having, as used herein, are defined as comprising (i.e., open language). Reference throughout this document to "one embodiment," "certain embodiments," "an embodiment," "an implementation," "an example," or similar terms means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the present disclosure. Thus, the appearances of such terms in various places throughout this specification are not necessarily all referring to the same embodiment. Furthermore, the particular features, structures, or characteristics may be combined in any suitable manner in one or more embodiments without limitation.

In accordance with embodiments, the present disclosure describes a method and apparatus that allows clinicians, health care professionals, and consumers to accurately and objectively assess dynamic pupil oscillations, which in part define pupil behavior, on a case-by-case basis. Furthermore, these dynamic pupil oscillations may be used in conjunction with a variety of pathology-specific algorithms that are specific to different drug signatures and physiological conditions in order to identify the pathology that results therefrom. In embodiments, the pathology-specific algorithm may be directed to alcohol, opioids, cannabinols, alpha-2 agonists, benzodiazepines, ketamine morphine, morphine-3-glucuronic acid, morphine-6-glucuronic acid, or combinations thereof, among others.

According to an exemplary embodiment of the present disclosure, the evaluation of dynamic pupil oscillations may be performed by a device or pupillometry apparatus that combines an imaging device with an imaging sensor, such as an infrared camera or CMOS sensor within a housing, with a display device, which may be a smartphone or a dedicated display module. In an embodiment, both the imaging device and the display device may be contained within a smart phone or similar mobile terminal. The connection to the display will enable the software application to objectively generate comparison information for dynamic pupil oscillations so that the software application can facilitate understanding of the comparison information. To this end, the device may be a screening tool, and the software application of the device may be algorithms and methods developed to specifically address a variety of clinical situations. These software applications enable objective measurement of dynamic pupil behavior in, for example, a clinical setting, and may be stored within a memory of a smartphone or device.

According to an embodiment, the above-described apparatus of the present disclosure may implement the method in conjunction with the additionally described hardware. For example, such hardware may be a chamber configured to adapt the smartphone to the face of the patient or user. To facilitate data acquisition, an exemplary imaging device or infrared camera may be capable of being adapted by additional described hardware to be ergonomically shaped to the patient's face for accurate pupil assessment. Furthermore, this allows the method to be implemented in a wide variety of environments, where the method can be performed by ubiquitous devices. In an embodiment, the method may be performed by a processing circuit configured to control an imaging device of a smartphone or other apparatus to acquire a video sequence of a human eye. Such video sequences may be acquired, for example, at a rate of 100 frames per second, although it should be appreciated that other frame rates may be used in order to obtain a pupil video sequence.

During real-world implementation, the above-described apparatus and method thereof according to exemplary embodiments may provide fast access to patient data, which may be an important tool in a variety of clinical situations. By including an integrated ready chamber that can be adjusted to the patient's face and a display dedicated to the collected information, in embodiments, convenient and mobile access to the patient's pupil data and analysis can be facilitated. To improve the adaptability of the method, specific algorithms can be deployed to interpret the acquired patient pupil data, which can be tailored to different clinical situations, allowing widespread use and access by a variety of professionals and general persons, including but not limited to medical professionals.

In various applications, the assessment of pupil oscillations may be applied to the identification of drug use. Identification of drug use provides one of the greatest opportunities for more widespread use of pupillometry. The drug has a specific effect on the autonomic nervous system, directly affecting the pupil and the pupillary oscillations. Examination of pupil oscillations (known as nystagmus) using, for example, spectral analysis, produces specific frequency-attributable responses. Drug use varies the spectral profile of iris tremor in a specific attributable manner. As introduced above, the devices and methods of the present disclosure may be an important means of understanding drug use relevance and evaluating patients for drug use status.

Additionally, and according to embodiments, the devices and methods of the present disclosure may be used to assess the function of the autonomic nervous system in the context of a physiological condition. It is known that pupil oscillations vary due to abnormal activity in the autonomic nervous system, such as the presence of autonomic imbalance. Thus, the function of the autonomic nervous system and its abnormal activity can be assessed by the apparatus and methods of the present disclosure, making the present disclosure an important means of assessing patients for the presence of specific physiological conditions.

Referring now to the drawings and as described above, according to embodiments, the present disclosure relates to an apparatus and method for determining the presence of a biologically active compound, drug or physiological perturbation in a patient. Briefly, the method comprises, for example, the steps of: (1) acquiring a video sequence of a patient's eye, the video sequence comprising a plurality of video frames; (2) detecting and measuring a pupil size in each of the plurality of video frames of the video sequence, wherein a size of the pupil size over time forms a pupil oscillation of the patient; (3) determining, using local or remote processing circuitry, a spectrum of detected and measured pupil sizes over time based on the pupil oscillations; and (4) determining, using the processing circuitry and based on the band power (i.e., the area under the curve) of the spectrum, the presence of a drug or physiological condition of the patient.

Referring now to fig. 1, and with additional details regarding the above, the method may include data processing 130, the data processing 130 first including acquisition 131 of a video sequence of a patient's eye as outlined in fig. 1, the video including a plurality of video frames. After acquisition 131, primary data (such as, for example, pupil size and resulting pupil oscillation) 132 may be determined for each of a plurality of video frames in a video sequence of a patient's eye.

Figure 2 is a graphical illustration of isolated pupil data isolating nystagmus prior to spectral analysis, according to an embodiment. In particular, amplitude pupil oscillations are shown over a 5 second period of pupil light reflection.

Returning now to fig. 1, data 133 defining the pupil oscillations may then be mined by local or remote processing circuitry to determine secondary data 134, which may include, for example, a spectrum of pupil oscillations over time. The spectrum of secondary data 134 determined to be pupil data 133 may then be provided as processed nystagmus data 135 to the methods of the present disclosure for evaluating the newly processed data. Alternatively or in combination, the pupil data may forego additional data manipulation 135' and the post-processing iris tremor data 134 may be defined immediately.

With regard to implementation of the methods of the present disclosure, the processed nystagmus data 134 may be accessed during the runtime of the method, where the processed nystagmus data 134 from the experimental pupil and the processed nystagmus data 134 from the reference pupil may be used to determine, among other things, the presence of a biologically active compound, drug, or other physiological perturbation of the patient. This may be, for example, determining the presence and/or level of alcohol-induced damage based on the frequency band power calculated from the spectrum.

Different applications (such as detection of drug use, or detection of physical conditions or physical disturbances) may consider different pupillometric measures and different weights or different ways of processing pupillometric measures.

The method of fig. 1 may be initiated, for example, by: (1) during an initial processing 132 of the video sequence, two points on the center of the pupil and the boundary of the pupil and the iris are located in a first frame of the plurality of frames; (2) generating, using processing circuitry, a mask image corresponding to an expected position of an iris based on the positioning, the mask image comprising a plurality of pixels; and (3) determining pupil size (i.e., primary data) and resulting pupil oscillation based on the generated mask image.

As described above, the acquired video sequence may be processed by a processor or cloud-based processing in the attachable apparatus (such as a smartphone, among other things). Although a smartphone is described herein in the context of the above-described processing circuitry and has been previously described, as evidenced by US 2015/0116665a1 and incorporated by reference herein, it can be appreciated that any processor, including an external processor or cloud-based processing circuitry, can be used to process the acquired video sequence.

In addition to the above, the acquired video sequence may include pupillary responses to, for example, glints. To produce such a reaction or pupil light reflection, a flash of light according to standardized lighting conditions may be provided by the flashlight of the aforementioned smart phone or similar mobile device.

As described above, pupil oscillations and/or the response to light may reflect the activity of the autonomic nervous system. For example, when exhibiting pupillary light reflex and reflecting the integrity of the autonomic nervous system, contraction or miosis occurs in response to a flash of light due to increased parasympathetic tone, while dilation or pupillary dilation reflects increased sympathetic tone. Pupillary light reflex can be assessed by the methods of the present disclosure and devices thereof, where higher frequency activation occurs with increased sympathetic tone, and lower frequency activation occurs due to increased parasympathetic tone. When applied in the real world, pupillary oscillations may be affected by the activity of certain bioactive compounds, drugs, physiological conditions interacting with receptors of the autonomic nervous system, thereby affecting either sympathetic or parasympathetic responses.

According to an embodiment, a variety of pupillometric measures may be evaluated from the pupil data after the initial video sequence processing 132, such that in combination with the secondary data 134 including the frequency spectrum, the patient response profile may be better characterized. There are at least six pupillometric measures used to generate an algorithm that can help determine a physiological characteristic, such as, for example, drug use or physical condition. At least two of the pupillometric measurements are static measurements and may include a baseline pupil size and a size of maximum constriction. These measures can be used to generate, for example, a contraction amplitude. As introduced above, the baseline pupil size may be found prior to the flash, and the most constricted size may be determined after the flash. At least four of the pupillometric measurements may be dynamic measurements and may be dynamic responses to glints, including contraction speed (average and maximum contraction speed), contraction latency, and re-dilation speed. With respect to the detection and identification of drug use or pathological conditions, various drugs and physical conditions affect various parameters of the pupillary light reflex in a predictable manner. Depending on the application of the measurement, any of at least six pupillometric measures may be a suitable indicator. When the application varies, such as detection of a particular drug use or detection of a particular physical condition, different pupillometric measures and different weights or different ways of processing pupillometric measures may be considered as appropriate.

According to an embodiment, the above-mentioned pupillometric measure or parameter may comprise at least one of a plurality of further parameters including the maximum pupil size, the maximum change in pupil size, the maximum speed of pupil re-dilation, the average speed of pupil re-dilation, the maximum area of the pupil, the minimum area of the pupil, the average area of the pupil, the time for the pupil size to recover to 75%, the time for the pupil size to recover to 100%, and the area under the pupil light reflection curve.

According to an embodiment, the secondary data 134 may include, for example, a frequency spectrum. The frequency spectrum may be derived from the pupil data by a frequency-based transformation method. Such frequency-based transform methods may be fast fourier transforms, Hilbert Huang transforms (Hilbert Huang transforms), and the like, as will be understood by those of ordinary skill in the art. From the frequency spectrum, a parameter may be determined, such as amplitude at a particular frequency or power of a frequency band across a range of frequencies, where the particular frequency or range of frequencies is related to a level of pathological activity. Furthermore, the spectrum can be unambiguously evaluated, wherein a mathematical model of the spectrum is related to the activity level of the pathology. To this end, heuristic models may be used in developing the algorithm.

During implementation of the above-described method, and referring now to fig. 3, selected parameters may be determined and compared against experimental data and reference data such that the presence and/or amount of a substance, drug, or physiological substance may be determined.

To do so, first, reference iris tremor data 335 ″ may be acquired from reference database 340, and experimental iris tremor data 335' may be acquired from, for example, the current patient. This iris tremor data is similar to the processed iris tremor data of fig. 1, where the method of fig. 1 has been applied to the acquired video sequence.

After the appropriate iris tremor data has been acquired, a first or experimental parameter 336' may be determined from experimental iris tremor data 335' of the patient's pupillary iris tremor. The experimental parameters 336' may be, but are not limited to, mathematical models of amplitude, frequency, band power, and waveform, as described above. In addition, the experimental parameters 336' may be, among other things, a baseline pupil size, a maximum pupil size, a minimum pupil size, a contraction velocity (average contraction velocity and maximum contraction velocity), a contraction waiting time, a re-expansion velocity, a maximum change in pupil size, a maximum velocity of pupil re-expansion, an average velocity of pupil re-expansion, a maximum area of the pupil, a minimum area of the pupil, an average area of the pupil, a time for the pupil size to return to 75%, a time for the pupil size to return to 100%, and an area under the pupil light reflection curve.

Similar to the above, the first parameter or reference parameter 336 "may be determined from reference iris tremor data 335" of a reference patient's pupillary iris tremor or a representative pupillary iris tremor of a population of patients. The reference parameters 336 "may be, but are not limited to, mathematical models of amplitude, frequency, band power, and waveform, as described above. In addition, the experimental parameters 336' may be, among other things, a baseline pupil size, a maximum pupil size, a minimum pupil size, a contraction velocity (average contraction velocity and maximum contraction velocity), a contraction waiting time, a re-expansion velocity, a maximum change in pupil size, a maximum velocity of pupil re-expansion, an average velocity of pupil re-expansion, a maximum area of the pupil, a minimum area of the pupil, an average area of the pupil, a time for the pupil size to return to 75%, a time for the pupil size to return to 100%, and an area under the pupil light reflection curve.

In an exemplary embodiment, the second parameter or comparison index 337 may be determined as a result of the calculation based on, for example, experimental parameters 337' and reference parameters 337 "determined from the patient's pupillary iris tremor and the reference patient's pupillary iris tremor, respectively. The comparison indicators may include, among other things, delta band power or the difference between the band power of the experimental data and the corresponding band power of the reference data, delta band power%, normalized delta band power, and a similarity ratio between mathematical models of the experimental data and the reference data.

In an embodiment, the comparison indicator 337 may be a correlation of the experimental waveform and the reference waveform, wherein a lack of correlation of the respective waveforms may or may not be indicative of a physiological condition.

After determining the comparison indicator 337, according to an embodiment, the comparison indicator 337 may be evaluated 338 relative to a predetermined threshold to determine the presence or absence of a biologically active substance, drug, or physiological perturbation. The bioactive substance, drug, or physiological perturbation as defined by the assessed comparative index may be indicated by a display.

For example, a patient may be suspected of recreational use of opioids, or specifically, methadone. If the delta band power is a comparative indicator and is determined to be substantial when comparing patient data to reference data for comparable patients in a frequency range associated with methadone users, it may be determined that the patient has been acutely exposed to methadone. In another example, a patient may be suspected of overuse of a prescribed opioid, such as hydrocodone. If the delta band power is within a frequency range associated with hydrocodone usage, determined to be substantial when comparing the patient's data to reference data from an expected hydrocodone band power user, it may be determined that the patient has been acutely overexposed to hydrocodone.

According to an embodiment, after comparing the indicator with respect to the selected criteria evaluation 338, the result or physiological condition may be displayed 339 via a display of the device described with reference to fig. 7, such that the user may be alerted to the normal or other condition of the patient.

The evaluation of the comparison index against the criteria may reflect an analysis of the pattern and correlation of the quantized spectrum that may predict a particular situation. Patterns and correlations can further predict drug interactions and their effects on pupillary iris tremor. According to embodiments, these patterns and correlations may be identified by comparison to a library of spectra associated with a specific bioactive compound or drug, a set of specific bioactive compounds or drugs, or a plurality of interacting bioactive compounds or drugs.

As discussed with respect to fig. 3, the comparison of unknown or experimental data to reference data may be performed by evaluating, for example, the amplitude at one or more or a set of particular frequencies along the frequency domain.

Thus, FIG. 4 provides a graphical representation of the spectral assessment of experimental and reference iris tremor data as would be performed during the generation of the secondary data in FIG. 1. As shown, experimental iris tremor data captured at the 'maximal opioid effect' time period is shown along with reference data shown as 'baseline'. The effect of opioid use can be observed for a single patient at varying frequencies across the spectrum, and from this, a concomitant analysis of parasympathetic and sympathetic activity can be inferred. As observed in fig. 4, for example, opioid use altered pupil oscillations between 8Hz and 11Hz, and high frequency pupil oscillations between 12Hz and 14Hz, as compared to baseline. In the case of high frequency pupillary oscillations, this change may be indicative of increased sympathetic tone in response to opioid exposure. In an example, identification of physiological perturbations can be performed by evaluating correlations between mathematical models of the plotted data.

The specificity demonstrated in fig. 4 is shown in fig. 5 for a number of drugs, where fast fourier transform data normalized to baseline for iris tremors for patients using opioids and patients using cannabis is presented. As can be observed, the amplitude change for each patient varies from baseline in the frequency range of 8Hz to 11Hz, for example, using cannabis can increase sympathetic tone, while using opioids can decrease sympathetic tone from baseline.

According to embodiments, experimental iris tremor data comprising unknown spectra may be analyzed or filtered with respect to specific target bioactive compounds. Such analysis or filtering may be based on prior studies of biologically active target compounds. Filtering may include, for example, removing data above, below, or within a predetermined frequency and removing data above, below, or at a predetermined amplitude, e.g., where the predetermined frequency and the predetermined amplitude are associated with a specific target bioactive compound. For example, it may be known that opioids may have increasing amplitude oscillations between 12Hz and 14Hz along the frequency domain, as shown in fig. 4. By determining the area under the curve between these two frequencies, referred to as the band power, the unknown spectral data can be compared to reference spectral data of a known entity to determine the delta band power. As discussed with respect to fig. 3, the delta band power may be a comparison indicator or second parameter, and if present, may be above a predetermined threshold based on the sensitivity of the data acquisition device.

Further, the comparison of the full longitudinal pupil response in the frequency domain may be compared to a spectral library by pattern recognition techniques employed in machine learning for determining irregularities in the data. Such a method may identify, for example, one or more amplitude inflection points in the frequency domain associated with one or more known bioactive compounds, drugs, or physiological conditions.

In addition to the above methods, and as indicated, each unknown spectrum can be analyzed with respect to the effects of multiple interacting bioactive compounds, thereby providing context for the effects of drug-drug interactions on the nervous system. For example, unknown spectral data may be filtered in the target context of pupillary action of alcohol interacting with opioids in order to isolate the compound.

Further, in embodiments, each unknown spectrum may be compared to a reference database of iris tremors, and it may be determined that one or more drug-drug interactions may be correlated with physical perturbations of the pupillary light reflex.

For example, the method may be applied such that alcohol use is assessed in one case and opioid use is assessed in a second case, respectively. In a third case, the effect of combined alcohol and opioid use can be assessed. Clinically significant frequency ranges or bands, such as 0.3 Hz-3.0 Hz or 3.1Hz-5.0Hz, can be evaluated to detect specific bioactive compounds, drugs, etc., where one frequency range or band indicates the presence of alcohol and another frequency range or band indicates opioid use. In the case where the combination of alcohol and opioid changes the effects that both would have separately, a filter may be applied to eliminate one spectrum from the spectrum so that the other spectrum may be detected and quantified. This may be a common situation in real world applications, where a first compound in a group of compounds may significantly outperform the group in terms of proximity to a specific receptor, thereby attenuating the effect of competing compounds while masking the presence of other compounds in the group and significantly altering the pupillary light reflex.

Furthermore, the band power determined on each of these bands may be indicative of the concentration of the biologically active substance or drug at calibration, providing a potentially powerful non-invasive means for drug usage detection and monitoring.

According to an embodiment, experimental iris tremor data containing unknown frequency spectra may be compared to each entry of reference iris tremor data of a reference database in order to identify unknown contributing factors to the frequency spectra. For example, one or more drugs may have an effect on the spectrum of data from experimental iris tremor. This spectrum may be compared to a reference database containing spectra affected by multiple drugs so that the presence and possibly identity of one or more drugs of the experimental iris tremor may be determined. Importantly, while computationally intensive, this approach does not require the user to predict one or more drugs in the spectrum of the experimental iris tremor, but rather can compare the unknown spectrum to a set of possible drug candidates.

According to embodiments, the unknown spectral data and the quantified spectral data may be specifically evaluated as compared to baseline to detect the presence of the bioactive compound. This method is useful when only the presence of specific biologically active compounds is considered. In embodiments, the baseline may be established from a reference database of multiple control patients, a previous control data set of the same patient, or a combination thereof.

In addition to the above, according to embodiments, the present methods may be used to detect autonomic abnormalities including a variety of conditions, including diabetic neuropathy and postural tachycardia syndrome (postural orthostatic tachycardiaa syndrome).

The method of this embodiment may also be used for management of drug use and monitoring thereof. Currently, drug dosage management is subjective, at the discretion of the clinician. The methods of the present disclosure may be applied to long-term or repeated drug monitoring, including detection of biologically active compounds and corresponding subsequent metabolites. According to this method, drug use and damage over time, including dose response effects, can be observed. Wherein the determined index is clinically useful for objective analysis.

Further, the method may be developed to function as a shunt test for drivers suspected of being affected by alcohol or controlled substances. If any spectrum unique to the illegal substance is found during the test, the driver will be submitted to other tests.

In addition to the above, the method of this embodiment may be further implemented for monitoring the postoperative sedation state of a surgical patient.

According to an embodiment, the method of this embodiment may also be used to differentiate between direct drug action on the pupil and analgesic effects (i.e., the method allows differentiation of drug and system related parameters by using elements of static or dynamic pupil parameters as pharmacokinetic analogs and the area under the pupil reflex dilation curve as analgesic pharmacodynamic analogs). The fast fourier transform derived "signature" of the present disclosure provides a non-invasive method for further informing such paradigms by indicating the presence of a substance.

In embodiments, the methods of the present disclosure, when combined with other features of the pupillary response including, but not limited to, pupillary light reflex and nerve-specific nerve stimulation-induced pupillary light reflex, can be used in the context of analgesic response or other drug action. This approach allows the isolation of drug-induced hyperalgesia or exposure-mediated sensitization of pain from increased pain sensitivity due to injury or disease progression.

Next, a description of hardware employing an apparatus or device according to an exemplary embodiment is described with reference to fig. 6. In fig. 6, the apparatus includes a CPU 600 that performs the above-described process. Process data and instructions may be stored in memory 602. The processes and instructions may also be stored on a storage media disk 604, such as a Hard Disk Drive (HDD) or portable storage media, or may be stored remotely. Additionally, the claimed advancements are not limited by the form of computer-readable media on which the instructions of the inventive process are stored. For example, the instructions may be stored on a CD, DVD, in flash memory, RAM, ROM, PROM, EPROM, EEPROM, hard disk, or any other information processing device with which the device communicates, such as a server or computer.

In addition, the claimed advancements may provide components of a utility application, a background daemon, or an operating system, or a combination thereof, that executes in conjunction with the CPU 600 and an operating system (such as Microsoft Windows 7, UNIX, Solaris, LINUX, Apple MAC-OS, and other systems known to those skilled in the art).

To implement the apparatus, the hardware elements may be implemented by various circuit elements known to those skilled in the art. For example, CPU 600 may be a Xeron or Core processor from Intel, USA, or an Opteron processor from AMD, USA, or may be other processor types as will be appreciated by those of ordinary skill in the art. Alternatively, CPU 600 may be implemented on an FPGA, ASIC, PLD, or using discrete logic circuitry, as one of ordinary skill in the art will recognize. Additionally, CPU 600 may be implemented as multiple processors working in parallel in concert to execute the instructions of the inventive process described above.

The apparatus of fig. 6 also includes a network controller 606 (such as an Intel ethernet PRO network interface card from Intel corporation of america) for interfacing with the network 650. As can be appreciated, network 650 may be a public network (such as the internet) or a private network (such as a LAN or WAN network), or any combination thereof, and may also include PSTN or ISDN sub-networks. The network 650 may also be wired (such as an ethernet network) or may be wireless (such as a cellular network, including EDGE, 3G, and 4G wireless cellular systems). The wireless network may also be WiFi, bluetooth, or any other form of wireless communication known.

The apparatus also includes a display controller 608 (such as an NVIDIA GeForceGTX or Quadro graphics adapter from NVIDIA corporation, usa) for interfacing with a display 610 (such as a Hewlett Packard HPL2445w LCD display). The general purpose I/O interface 612 interfaces with a keyboard and/or mouse 614 and a touch screen panel 616 on or separate from the display 610. The general purpose I/O interface also connects to a variety of peripherals 618, including printers and scanners, such as OfficeJet or DeskJet from Hewlett Packard.

A Sound controller 620 (such as Sound blast X-FiTitanium from Creative) is also provided in the device for interfacing with a speaker/microphone 622 to provide Sound and/or music.

The general purpose memory controller 624 connects the storage media disk 604 with a communication bus 626, which may be an ISA, EISA, VESA, PCI, or the like, for interconnecting all of the components of the device. Since the general features and functionality of the display 610, keyboard and/or mouse 614, and display controller 608, storage controller 624, network controller 606, sound controller 620, and general purpose I/O interface 612 are known, a description of such features is omitted herein for the sake of brevity.

Embodiments of the present disclosure may also be set forth as in the additional description below.

(1) An apparatus for assessing pupillary tremor of a patient, comprising: a display; and processing circuitry configured to transform experimental data and reference data of the pupil iris tremor of the patient by a frequency-based transformation; calculating a first parameter of the one or more selected parameters based on transformed experimental data of the pupil iris tremor of the patient; calculating a corresponding first parameter of the one or more selected parameters based on the transformed reference data; generating an indicator from the first parameter based on the experimental data and the corresponding first parameter based on the reference data, the generated indicator being a normalization of the first parameter and the corresponding first parameter; determining whether the generated indicator reaches a predetermined threshold, the predetermined threshold being related to a biological activity goal; and displaying, on the display and based on the determination, the assessment of the pupillary iris tremor of the patient, wherein the assessment of the pupillary iris tremor of the patient is an identification of an opioid as the bioactive target.

(2) The apparatus of (1), wherein the processing circuit is further configured to determine whether the generated indicator reaches the predetermined threshold based on a correlation between the first parameter of the experimental data and the corresponding first parameter of the reference data.

(3) The apparatus of (1) or (2), wherein the first parameter based on the experimental data is an amplitude at a predetermined frequency.

(4) The apparatus according to any one of (1) to (3), wherein the first parameter based on the experimental data is a band power.

(5) The apparatus of any one of (1) to (4), wherein the generated indicator is a difference between a band power of the experimental data and a band power of the reference data.

(6) The apparatus of any of (1) to (5), wherein the first parameter based on the experimental data is a mathematical model of the experimental data.

(7) The apparatus according to any one of (1) to (6), wherein the first parameter based on the experimental data is a mathematical model of a spectrum of the experimental data.

(8) The apparatus of any one of (1) to (7), wherein the generated indicator is a similarity ratio of a mathematical model of a spectrum of the experimental data to a mathematical model of a spectrum of the reference data.

(9) The device of any of (1) to (8), wherein the processing circuitry is further configured to acquire a plurality of video sequences of the patient's eye; generating pupil data based on primary data calculated from the plurality of video sequences, the primary data including a time-based pupil size; and calculating secondary data from the generated pupil data, wherein the secondary data comprises the spectrum of the pupillary tremor.

(10) The device of any of (1) to (9), wherein the primary data is calculated based on a mask image, to generate the mask image, the processing circuitry is further configured to locate a center of a pupil of the eye, a boundary of the pupil of the eye, and an iris of the eye; and generating the mask image based on the center of the pupil of the eye, the boundary of the pupil of the eye, and the location of the iris of the eye, the mask image corresponding to an expected location of the iris.

(11) An apparatus for assessing pupillary tremor of a patient, comprising a display; and processing circuitry configured to calculate a first parameter of the one or more selected parameters based on experimental data of the pupil iris tremor of the patient; calculating a corresponding first parameter of the one or more selected parameters based on reference data of pupillary iris tremor; generating an indicator from the first parameter based on the experimental data and the corresponding first parameter based on the reference data, the generated indicator being a normalization of the first parameter and the corresponding first parameter; determining whether the generated indicator reaches a predetermined threshold, the predetermined threshold being related to a biological activity goal; and displaying the assessment of the pupil irises of the patient on a display and based on the determination.

(12) The apparatus of (11), wherein the processing circuit is further configured to determine whether the generated indicator reaches the predetermined threshold based on a correlation between the first parameter of the experimental data and the corresponding first parameter of the reference data.

(13) The device of any of (11) or (12), wherein the processing circuitry is further configured to transform the experimental data and the reference data of the pupillary iris tremor of the patient by a frequency-based transformation, and the generated indicator is a difference between a band power of the experimental data and a band power of the reference data.

(14) The apparatus of any one of (11) to (13), wherein the processing circuitry is further configured to transform the experimental data and the reference data of the pupillary iris tremor of the patient by a frequency-based transformation, and the generated indicator is a similarity ratio of a mathematical model of a spectrum of the experimental data to a mathematical model of a spectrum of the reference data.

(15) The device of any of (11) to (14), wherein the processing circuitry is further configured to acquire a plurality of video sequences of the patient's eye; generating pupil data based on primary data calculated from the plurality of video sequences, the primary data including a time-based pupil size; and calculating secondary data from the generated pupil data, wherein the secondary data comprises the spectrum of the pupillary tremor.

(16) The device of any of (11) to (15), wherein the primary data is calculated based on a mask image, to generate the mask image, the processing circuitry is further configured to locate a center of a pupil of the eye, a boundary of the pupil of the eye, and an iris of the eye; and generating the mask image based on the center of the pupil of the eye, the boundary of the pupil of the eye, and the location of the iris of the eye, the mask image corresponding to an expected location of the iris.

(17) The apparatus of any one of (11) through (16), wherein the assessment of the pupil iris tremor of the patient is identification of the bioactive target selected from the group consisting of alcohol, opioid, cannabinol, alpha-2 agonist, benzodiazepine, ketamine morphine, morphine-3-glucuronic acid, morphine-6-glucuronic acid or combinations thereof.

(18) The apparatus of any one of (11) to (17), wherein the processing circuitry is further configured to transform the experimental data of the pupillary iris tremor of the patient by frequency-based transformation, and to remove data according to a predetermined frequency range from the transformed experimental data.

(19) The device of any one of (11) to (18), wherein the assessment of the pupillary iris tremor of the patient may be identification of the presence of an autonomic abnormality, the autonomic abnormality being one selected from the group consisting of postural tachycardia syndrome and diabetic neuropathy.

(20) An apparatus for assessing pupil irises of a patient, comprising processing circuitry configured to calculate a first parameter of one or more selected parameters based on experimental data of the pupil irises of the patient; calculating a corresponding first parameter of the one or more selected parameters based on reference data of pupillary iris tremor; generating an indicator from the first parameter based on the experimental data and the corresponding first parameter based on the reference data, the generated indicator being a normalization of the first parameter and the corresponding first parameter; determining whether the generated indicator reaches a predetermined threshold, the predetermined threshold being related to a biological activity goal; and displaying the assessment of the pupil irises of the patient on a display and based on the determination.

Accordingly, the foregoing discussion discloses and describes merely exemplary embodiments of the present invention. As will be understood by those skilled in the art, the present invention may be embodied in other specific forms without departing from the spirit or essential characteristics thereof. Accordingly, the disclosure of the present invention is intended to be illustrative, but not limiting, of the scope of the invention, as well as other claims. This disclosure, including any readily discernible variants of the teachings herein, defines, in part, the scope of the foregoing claim terminology such that no inventive subject matter is dedicated to the public.

Claims

1. An apparatus for assessing pupillary tremor of a patient, comprising:

a display; and

a processing circuit configured to

Transforming experimental data and reference data of the pupil nystagmus of the patient by a frequency-based transformation,

calculating a first parameter of the one or more selected parameters based on transformed experimental data of the pupil iris tremor of the patient,

calculating a corresponding first parameter of the one or more selected parameters based on the transformed reference data,

generating an indicator from the first parameter based on the experimental data and the corresponding first parameter based on the reference data, the generated indicator being a normalization of the first parameter and the corresponding first parameter,

determining whether the generated indicator reaches a predetermined threshold, the predetermined threshold being related to a biological activity target, an

Displaying, on the display and based on the determination, the assessment of the pupil irises of the patient, wherein

The assessment of the patient's pupillary iris tremor is the identification of opioids as the bioactive target.

2. The apparatus of claim 1, wherein the processing circuit is further configured to determine whether the generated metric reaches the predetermined threshold based on a correlation between the first parameter of the experimental data and the corresponding first parameter of the reference data.

3. The apparatus of claim 1, wherein the first parameter based on the experimental data is an amplitude at a predetermined frequency.

4. The apparatus of claim 1, wherein the first parameter based on the experimental data is a band power.

5. The apparatus of claim 1, wherein the generated indicator is a difference between a band power of the experimental data and a band power of the reference data.

6. The apparatus of claim 1, wherein the first parameter based on the experimental data is a mathematical model of the experimental data.

7. The apparatus of claim 6, wherein the first parameter based on the experimental data is a mathematical model of a spectrum of the experimental data.

8. The apparatus of claim 1, wherein the generated indicator is a similarity ratio of a mathematical model of a spectrum of the experimental data to a mathematical model of a spectrum of the reference data.

9. The device of claim 1, wherein the processing circuit is further configured to

Acquiring a plurality of video sequences of the patient's eye,

generating pupil data based on primary data calculated from the plurality of video sequences, the primary data including a time-based pupil size, and

calculating secondary data from the generated pupil data, wherein

The secondary data includes the frequency spectrum of the pupillary iris tremor.

10. The apparatus of claim 9, wherein the primary data is calculated based on a mask image, to generate the mask image, the processing circuitry is further configured to

Locating a center of a pupil of the eye, a boundary of the pupil of the eye, and an iris of the eye, and

generating the mask image based on the center of the pupil of the eye, the boundary of the pupil of the eye, and the location of the iris of the eye, the mask image corresponding to an expected location of the iris.

11. An apparatus for assessing pupillary tremor of a patient, comprising:

a display; and

a processing circuit configured to

Calculating a first parameter of the one or more selected parameters based on experimental data of the pupil nystagmus of the patient,

calculating a corresponding first parameter of the one or more selected parameters based on reference data of pupil iris tremor,

Displaying, on the display and based on the determination, the assessment of the pupil irises of the patient.

12. The apparatus of claim 11, wherein the processing circuit is further configured to

Determining whether the generated indicator reaches the predetermined threshold based on a correlation between the first parameter of the experimental data and the corresponding first parameter of the reference data.

13. The apparatus of claim 11, wherein the processing circuit is further configured to

Transforming the experimental data and the reference data of the patient's pupillary nystagmus by a frequency-based transformation, and

the generated indicator is a difference between a band power of the experimental data and a band power of the reference data.

14. The apparatus of claim 11, wherein the processing circuit is further configured to

the generated indicator is a similarity ratio of a mathematical model of a spectrum of the experimental data to a mathematical model of a spectrum of the reference data.

15. The apparatus of claim 11, wherein the processing circuit is further configured to

Acquiring a plurality of video sequences of the patient's eye,

calculating secondary data from the generated pupil data, wherein

16. The apparatus of claim 15, wherein the primary data is calculated based on a mask image, to generate the mask image, the processing circuitry is further configured to

17. The apparatus of claim 11, wherein the assessment of the patient's pupillary iris tremor is identification of the bioactive target selected from the group consisting of alcohol, opioids, cannabinol, alpha-2 agonists, benzodiazepines, ketamine morphine, morphine-3-glucuronic acid, morphine-6-glucuronic acid, or combinations thereof.

18. The apparatus of claim 11, wherein the processing circuit is further configured to

Transforming the experimental data of the patient's pupillary nystagmus by a frequency-based transformation, and

data according to a predetermined frequency range is removed from the transformed experimental data.

19. The device of claim 11, wherein the assessment of the patient's pupillary iris tremor may be the identification of the presence of an autonomic abnormality selected from one of the group consisting of postural tachycardia syndrome and diabetic neuropathy.

20. An apparatus for assessing pupillary tremor of a patient, comprising:

a processing circuit configured to

Displaying, on a display and based on the determination, the assessment of the pupil irises of the patient.